Integrative neuroscience: linking levels of analyses

If we are to understand how the brain performs different integrated functions in cellular terms, we need both to understand all relevant levels of analysis from the molecular to the behavioural and cognitive levels and to realize an integration of such levels. This is currently a major challenge for neuroscience. Most research, whether dealing with perception, action or learning, focuses on a few levels of organization, for instance the molecular level and brain imaging, and leaves other crucial areas practically untouched. To reach the level of understanding that we desire, a multi-level approach is required in which the different levels link into each other. It is possible to bridge across the different levels for one system, and this has been demonstrated, for example, in the lamprey in generation of goal-directed locomotion. It can be argued that an integrated analysis of any neural system cannot be performed without the aid of a close interaction between experiments and modelling. The dynamic processing within any neural system is such that an intuitive interpretation is rarely sufficient.     

Biophysical models of fMRI responses

Functional magnetic resonance imaging (fMRI) is used to investigate where the neural implementation of specific cognitive processes occurs. The standard approach uses linear convolution models that relate experimentally designed inputs, through a haemodynamic response function, to observed blood oxygen level dependent (BOLD) signals. Such models are, however, blind to the causal mechanisms that underlie observed BOLD responses. Recent developments have focused on how BOLD responses are generated and include biophysical input-state-output models with neural and haemodynamic state equations and models of functional integration that explain local dynamics through interactions with remote areas. Forward models with parameters at the neural level, such as dynamic causal modelling, combine both approaches, modelling the whole causal chain from external stimuli, via induced neural dynamics, to observed BOLD responses.     

Information processing in large-scale cerebral networks: the causal connectivity approach

Today, cognitive functions are considered to be the offspring of the activity of large-scale networks of functionally interconnected cerebral regions. The interpretation of cerebral activation data provided by functional imaging has therefore recently moved to the search for the effective connectivity of activated regions, which aims at understanding the role of anatomical links in the activation propagation. Our assumption is that only causal connectivity can offer a real understanding of the links between brain and mind. Causal connectivity is based on the anatomical connection pattern, the information processing within cerebral regions and the causal influences that connected regions exert on each other. In our approach, the information processing within a region is implemented by a causal network of functional primitives, which are the interpretation of integrated biological properties. Our choice of a qualitative representation of information reflects the fact that cerebral activation data are only the approximate view, provided by imaging techniques, of the real cerebral activity. This explicit modeling approach allows the formulation and the simulation of functional and physiological assumptions about activation data. Two alternative models explaining results of the striate cortex activation described by Fox and Raichle (Fox PT, Raichle ME (1984) J. Neurophysiol 51:1109–1120; Fox PT, Raichle ME (1985) Ann Neurol 17:303–305) are provided as an example of our approach. Received: 22 December 1998 / Accepted in revised form: 23 June 1999     

Integrated information in discrete dynamical systems: motivation and theoretical framework

This paper introduces a time- and state-dependent measure of integrated information, , which captures the repertoire of causal states available to a system as a whole. Specifically, quantifies how much information is generated (uncertainty is reduced) when a system enters a particular state through causal interactions among its elements, above and beyond the information generated independently by its parts. Such mathematical characterization is motivated by the observation that integrated information captures two key phenomenological properties of consciousness: (i) there is a large repertoire of conscious experiences so that, when one particular experience occurs, it generates a large amount of information by ruling out all the others; and (ii) this information is integrated, in that each experience appears as a whole that cannot be decomposed into independent parts. This paper extends previous work on stationary systems and applies integrated information to discrete networks as a function of their dynamics and causal architecture. An analysis of basic examples indicates the following: (i) varies depending on the state entered by a network, being higher if active and inactive elements are balanced and lower if the network is inactive or hyperactive. (ii) varies for systems with identical or similar surface dynamics depending on the underlying causal architecture, being low for systems that merely copy or replay activity states. (iii) varies as a function of network architecture. High values can be obtained by architectures that conjoin functional specialization with functional integration. Strictly modular and homogeneous systems cannot generate high because the former lack integration, whereas the latter lack information. Feedforward and lattice architectures are capable of generating high but are inefficient. (iv) In Hopfield networks, is low for attractor states and neutral states, but increases if the networks are optimized to achieve tension between local and global interactions. These basic examples appear to match well against neurobiological evidence concerning the neural substrates of consciousness. More generally, appears to be a useful metric to characterize the capacity of any physical system to integrate information.     

Investigating the biology of consciousness.

The fact that consciousness is a private, first-person phenomenon makes it more difficult to study than other cognitive phenomena that, although being equally private, also have characteristic behavioural signatures. Nonetheless, by combining cognitive and neurobiological methods, it is possible to approach consciousness, to describe its cognitive nature, its behavioural correlates, its possible evolutionary origin and functional role; last but not least, it is possible to investigate its neuroanatomical and neurophysiological underpinnings. In this brief essay I distinguish between two kinds of consciousness: core consciousness and extended consciousness. Core consciousness corresponds to the transient process that is incessantly generated relative to any object with which an organism interacts, and during which a transient core self and transient sense of knowing are automatically generated. Core consciousness requires neither language nor working memory, and needs only a brief short-term memory. Extended consciousness is a more complex process. It depends on the gradual build-up of an autobiographical self, a set of conceptual memories pertaining to both past and anticipated experiences of an individual, and it requires conventional memory. Extended consciousness is enhanced by language.     

Practical measures of integrated information for time-series data

A recent measure of 'integrated information', Φ(DM), quantifies the extent to which a system generates more information than the sum of its parts as it transitions between states, possibly reflecting levels of consciousness generated by neural systems. However, Φ(DM) is defined only for discrete Markov systems, which are unusual in biology; as a result, Φ(DM) can rarely be measured in practice. Here, we describe two new measures, Φ(E) and Φ(AR), that overcome these limitations and are easy to apply to time-series data. We use simulations to demonstrate the in-practice applicability of our measures, and to explore their properties. Our results provide new opportunities for examining information integration in real and model systems and carry implications for relations between integrated information, consciousness, and other neurocognitive processes. However, our findings pose challenges for theories that ascribe physical meaning to the measured quantities.     

Protein folding: from the levinthal paradox to structure prediction.

This article is a personal perspective on the developments in the field of protein folding over approximately the last 40 years. In addition to its historical aspects, the article presents a view of the principles of protein folding with particular emphasis on the relationship of these principles to the problem of protein structure prediction. It is argued that despite much that is new, the essential elements of our current understanding of protein folding were anticipated by researchers many years ago. These elements include the recognition of the central importance of the polypeptide backbone as a determinant of protein conformation, hierarchical protein folding, and multiple folding pathways. Important areas of progress include a detailed characterization of the folding pathways of a number of proteins and a fundamental understanding of the physical chemical forces that determine protein stability. Despite these developments, fold prediction algorithms still encounter difficulties in identifying the correct fold for a given sequence. This may be due to the possibility that the free energy differences between at least a few alternate conformations of many proteins are not large. Significant progress in protein structure prediction has been due primarily to the explosive growth of sequence and structural databases. However, further progress is likely to depend in part on the ability to combine information available from databases with principles and algorithms derived from physical chemical studies of protein folding. An approach to the integration of the two areas is outlined with specific reference to the PrISM program that is a fully integrated sequence/structural-analysis/fold-recognition/homology model building software system.     

Evolution of Integrated Causal Structures in Animats Exposed to Environments of Increasing Complexity

Natural selection favors the evolution of brains that can capture fitness-relevant features of the environment''s causal structure. We investigated the evolution of small, adaptive logic-gate networks (“animats”) in task environments where falling blocks of different sizes have to be caught or avoided in a ‘Tetris-like’ game. Solving these tasks requires the integration of sensor inputs and memory. Evolved networks were evaluated using measures of information integration, including the number of evolved concepts and the total amount of integrated conceptual information. The results show that, over the course of the animats'' adaptation, i) the number of concepts grows; ii) integrated conceptual information increases; iii) this increase depends on the complexity of the environment, especially on the requirement for sequential memory. These results suggest that the need to capture the causal structure of a rich environment, given limited sensors and internal mechanisms, is an important driving force for organisms to develop highly integrated networks (“brains”) with many concepts, leading to an increase in their internal complexity.     

Granger causality analysis of steady-state electroencephalographic signals during propofol-induced anaesthesia

Changes in conscious level have been associated with changes in dynamical integration and segregation among distributed brain regions. Recent theoretical developments emphasize changes in directed functional (i.e., causal) connectivity as reflected in quantities such as 'integrated information' and 'causal density'. Here we develop and illustrate a rigorous methodology for assessing causal connectivity from electroencephalographic (EEG) signals using Granger causality (GC). Our method addresses the challenges of non-stationarity and bias by dividing data into short segments and applying permutation analysis. We apply the method to EEG data obtained from subjects undergoing propofol-induced anaesthesia, with signals source-localized to the anterior and posterior cingulate cortices. We found significant increases in bidirectional GC in most subjects during loss-of-consciousness, especially in the beta and gamma frequency ranges. Corroborating a previous analysis we also found increases in synchrony in these ranges; importantly, the Granger causality analysis showed higher inter-subject consistency than the synchrony analysis. Finally, we validate our method using simulated data generated from a model for which GC values can be analytically derived. In summary, our findings advance the methodology of Granger causality analysis of EEG data and carry implications for integrated information and causal density theories of consciousness.     

The sense of consciousness

I propose that consciousness might be understood as the property of a system that functions as a sense in the biological meaning of that term. The theory assumes that, as a complex system, the sense of consciousness is not a fixed structure but implies structure with variations and that it evolved, as many new functions do, through the integration of simpler systems. The recognized exteroceptive and enteroceptive senses provide information about the organism's environment and about the organism itself that are important to adaptation. The sense of consciousness provides information about the brain and thus about the organism and its environment. It senses other senses and processes in the brain, selecting and relating components into a form that "makes sense"-where making sense is defined as being useful to the organism in its adaptation to the environment. The theory argues that this highly adaptive organizing function evolved with the growing complexity of the brain and that it might have helped resolve discrepancies created at earlier stages. Neural energies in the brain that are the input to the sense of consciousness, along with the processing subsystem of which they are a part, constitute the base of consciousness. Consciousness itself is an emergent effect of an organizing process achieved through the sense of consciousness. The sense of consciousness thus serves an organizing function although it is not the only means of organization in the brain. Its uniqueness lies in the character of the organization it creates with consciousness as a property of that organization. The paper relates the theory to several general conceptions-interactionism, epiphenomenalism and identity theory-and illustrates a number of testable hypotheses. Viewing consciousness as a property of a sense provides a degree of conceptual integration. Much of what we know about the evolution and role of the conventionally recognized senses should help us understand the evolution and role of the sense of consciousness, and of consciousness itself.     

Fusing EEG and fMRI based on a bottom-up model: inferring activation and effective connectivity in neural masses

The elucidation of the complex machinery used by the human brain to segregate and integrate information while performing high cognitive functions is a subject of imminent future consequences. The most significant contributions to date in this field, known as cognitive neuroscience, have been achieved by using innovative neuroimaging techniques, such as electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI), which measure variations in both the time and the space of some interpretable physical magnitudes. Extraordinary maps of cerebral activation involving function-restricted brain areas, as well as graphs of the functional connectivity between them, have been obtained from EEG and fMRI data by solving some spatio-temporal inverse problems, which constitutes a top-down approach. However, in many cases, a natural bridge between these maps/graphs and the causal physiological processes is lacking, leading to some misunderstandings in their interpretation. Recent advances in the comprehension of the underlying physiological mechanisms associated with different cerebral scales have provided researchers with an excellent scenario to develop sophisticated biophysical models that permit an integration of these neuroimage modalities, which must share a common aetiology. This paper proposes a bottom-up approach, involving physiological parameters in a specific mesoscopic dynamic equations system. Further observation equations encapsulating the relationship between the mesostates and the EEG/fMRI data are obtained on the basis of the physical foundations of these techniques. A methodology for the estimation of parameters from fused EEG/fMRI data is also presented. In this context, the concepts of activation and effective connectivity are carefully revised. This new approach permits us to examine and discuss some future prospects for the integration of multimodal neuroimages.     

Language and learning: the cognitive revolution at 60-odd

Around the middle of the last century, the prevailing psychological paradigm of behaviourism was challenged by what is now known as the cognitive revolution. Behaviourists viewed learning as changes in patterns of behaviour through reinforcement. By contrast, advocates of the cognitive approach argued that such behavioural changes were outward manifestations of computational operations on mental representations. Here we consider the current state of the cognitive revolution, focusing on the two most contentious issues in the debate: language and learning. The cognitive approach has proved to be extremely fruitful in both fields. Although contemporary learning theory has almost completely embraced the cognitive approach, the study of language has witnessed a clear empiricist trend to revert back to a kind of neo-behaviourism. Many contemporary authors contend that language is a means of communication that is learned solely through the observation of external events, and culturally transmitted to successive generations. Here, we argue that learning and language can only be properly understood from a cognitive perspective, where the mind is conceived of as a biologically underpinned computational system. As is the case in learning theory, there is abundant evidence showing that language is subserved by an autonomous cognitive system in the mind. We conclude that the cognitive revolution has fundamentally changed our understanding of the mind.     

基于听觉刺激的意识障碍患者脑功能连通性研究

目的 目前对意识障碍（DOC）患者的分级评估仍是相关领域的重点和难点。因效性网络可以通过时间序列间的因果关系直观地反映信息传递方向,帮助人们更好地理解患者大脑不同区域之间的信息交互作用。本文结合脑电图和因效性网络探讨听觉刺激下无反应觉醒综合征（VS）患者与最低意识状态（MCS）患者的脑功能连通性差异。方法 共纳入23例DOC患者,采集并分析唤名刺激下的脑电信号,通过多元格兰杰因果方法构建脑功能网络,利用脑网络节点度、聚类系数、全局效率以及因果流向性等参量从脑区之间协同工作的角度对比研究听觉刺激下不同意识水平患者的网络特征。结果 唤名刺激下MCS患者的脑功能连通性强于VS患者,且呈现出因果流向差异,MCS与VS患者四个脑区的信息传递方向均不相同。结论 唤名听觉刺激下MCS患者的信息传递能力强于VS患者;与VS患者相比MCS患者为因果源的电极通道数增多,对其他脑区的信息输出增多。本研究可为DOC患者意识水平的分级评估提供一定的理论依据。     

Qualia: The Geometry of Integrated Information

According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale—a shape that completely and univocally characterizes the quality of a conscious experience. Φ— the height of this shape—is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the “redness” of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating qualitative properties of experience into mathematics.     

Information measures for conscious experience

This paper argues that consciousness is integrated information, and introduces measures to assess it. These measures lead to the prediction that a physical system such as the brain gives rise to consciousness when some of its elements constitute a complex having high minimum information midpartition (MID) complexity.     

From the Phenomenology to the Mechanisms of Consciousness: Integrated Information Theory 3.0

This paper presents Integrated Information Theory (IIT) of consciousness 3.0, which incorporates several advances over previous formulations. IIT starts from phenomenological axioms: information says that each experience is specific – it is what it is by how it differs from alternative experiences; integration says that it is unified – irreducible to non-interdependent components; exclusion says that it has unique borders and a particular spatio-temporal grain. These axioms are formalized into postulates that prescribe how physical mechanisms, such as neurons or logic gates, must be configured to generate experience (phenomenology). The postulates are used to define intrinsic information as “differences that make a difference” within a system, and integrated information as information specified by a whole that cannot be reduced to that specified by its parts. By applying the postulates both at the level of individual mechanisms and at the level of systems of mechanisms, IIT arrives at an identity: an experience is a maximally irreducible conceptual structure (MICS, a constellation of concepts in qualia space), and the set of elements that generates it constitutes a complex. According to IIT, a MICS specifies the quality of an experience and integrated information Φ^Max its quantity. From the theory follow several results, including: a system of mechanisms may condense into a major complex and non-overlapping minor complexes; the concepts that specify the quality of an experience are always about the complex itself and relate only indirectly to the external environment; anatomical connectivity influences complexes and associated MICS; a complex can generate a MICS even if its elements are inactive; simple systems can be minimally conscious; complicated systems can be unconscious; there can be true “zombies” – unconscious feed-forward systems that are functionally equivalent to conscious complexes.     

Identifying causal genes and dysregulated pathways in complex diseases

In complex diseases, various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations are assumed to dys-regulate the same cellular pathways. Such a pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. First, we identified a representative set of genes that are differentially expressed in cancer compared to non-tumor control cases. Assuming that disease-associated gene expression changes are caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. We discovered a set of putative causal genes that potentially play a role in the disease. Combining an expression Quantitative Trait Loci (eQTL) analysis with pathway information, our approach allowed us not only to identify potential causal genes but also to find intermediate nodes and pathways mediating the information flow between causal and target genes. Our results indicate that different genomic perturbations indeed dys-regulate the same functional pathways, supporting a pathway-centric perspective of cancer. While copy number alterations and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.     

Toward computational systems biology

The development and successful application of high-throughput technologies are transforming biological research. The large quantities of data being generated by these technologies have led to the emergence of systems biology, which emphasizes large-scale, parallel characterization of biological systems and integration of fragmentary information into a coherent whole. Complementing the reductionist approach that has dominated biology for the last century, mathematical modeling is becoming a powerful tool to achieve an integrated understanding of complex biological systems and to guide experimental efforts of engineering biological systems for practical applications. Here I give an overview of current mainstream approaches in modeling biological systems, highlight specific applications of modeling in various settings, and point out future research opportunities and challenges.     

Knowledge-based vision and simple visual machines.

The vast majority of work in machine vision emphasizes the representation of perceived objects and events: it is these internal representations that incorporate the ''knowledge'' in knowledge-based vision or form the ''models'' in model-based vision. In this paper, we discuss simple machine vision systems developed by artificial evolution rather than traditional engineering design techniques, and note that the task of identifying internal representations within such systems is made difficult by the lack of an operational definition of representation at the causal mechanistic level. Consequently, we question the nature and indeed the existence of representations posited to be used within natural vision systems (i.e. animals). We conclude that representations argued for on a priori grounds by external observers of a particular vision system may well be illusory, and are at best place-holders for yet-to-be-identified causal mechanistic interactions. That is, applying the knowledge-based vision approach in the understanding of evolved systems (machines or animals) may well lead to theories and models that are internally consistent, computationally plausible, and entirely wrong.     

Insulation for daydreams: a role for tonic norepinephrine in the facilitation of internally guided thought

Although consciousness can be brought to bear on both perceptual and internally generated information, little is known about how these different cognitive modes are coordinated. Here we show that between-participant variance in thoughts unrelated to the task being performed (known as task unrelated thought, TUT) is associated with longer response times (RT) when target presentation occurs during periods when baseline Pupil Diameter (PD) is increased. As behavioral interference due to high baseline PD can reflect increased tonic activity in the norepinephrine system (NE), these results might implicate high tonic NE activity in the facilitation of TUTs. Based on these findings, it is hypothesised that high tonic mode NE leads to a generalised de-amplification of task relevant information that prioritses internally generated thought and insulates it from the potentially disruptive events taking place in the external environment.